Teaching Statistics最新文献

{"title":"Lessons from the famous 17th‐century paradox of the Chevalier de Méré","authors":"José Daniel López‐Barrientos, Eliud Silva, Enrique Lemus-Rodríguez","doi":"10.1111/test.12321","DOIUrl":"https://doi.org/10.1111/test.12321","url":null,"abstract":"We take advantage of a combinatorial misconception and the famous paradox of the Chevalier de Méré to present the multiplication rule for independent events; the principle of inclusion and exclusion in the presence of disjoint events; the median of a discrete‐type random variable, and a confidence interval for a large sample. Moreover, we pay tribute to our original bibliographic sources by providing two computational tools to facilitate the students' insights on these topics.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43963666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The curse of knowledge when teaching statistics","authors":"Itamar Shatz","doi":"10.1111/test.12320","DOIUrl":"https://doi.org/10.1111/test.12320","url":null,"abstract":"When teaching statistics, educators sometimes overestimate their students' knowledge and abilities. This is due to the curse of knowledge, a cognitive bias that causes people—especially experts—to overestimate how likely others are to know and understand the same things as them. This can lead to various issues, including struggling to communicate with students, and making students feel less comfortable in the classroom. To address this, educators should first identify situations where this bias can affect their teaching. In doing so, they should consider relevant risk factors, and potentially also solicit feedback from relevant individuals. Then, educators can reduce this bias and its impact on their teaching by using techniques such as keeping the curse of knowledge and their audience in mind, assessing students' knowledge, assuming lack of knowledge unless there is strong evidence to the contrary, and avoiding saying that things are obvious.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46729850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring a complex gender wage gap dataset: an introductory activity in identifying issues and data visualization","authors":"Robert S. Lasater, Anny-Claude Joseph, Kevin Cummiskey","doi":"10.1111/test.12319","DOIUrl":"https://doi.org/10.1111/test.12319","url":null,"abstract":"In this paper, we provide instructors with an approach for a classroom activity for students in an introductory data science or statistics course who have little or no statistical programming experience. We designed this activity to help students improve their statistical literacy while exploring a social justice problem‐the gender wage gap. To minimize the challenges of developing statistical literacy in students who lack programming skills, we developed a web‐based data visualization application that does not require users to have any prior programming knowledge. The data in this visualization application comes from the March 2018 Current Population Uniform Extracts detailed by the Center for Economic Policy Research. Students can use the visualization application to create tables and plots to explore data on factors such as earnings and gender. Instructors can also use the application for other wage‐related variables, such as race, occupation and family size.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47891591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What do you, the reader, want?","authors":"H. MacGillivray","doi":"10.1111/test.12316","DOIUrl":"https://doi.org/10.1111/test.12316","url":null,"abstract":"Recently, I was fascinated to see the original trust deed of 1978 setting up the Teaching Statistics Trust, which stated that, as part of the purpose “for the public benefit”, the Trust's aim was to set up a Journal to be “devoted to the dissemination of educative information about statistics and the teaching of statistics...”. The aim inside the front cover of the first issue in 1979 [1], included words that have been retained to this day within the current Aims and Scope. In particular, the words in bold in the following current statement were in the original Aim: “Teaching Statistics seeks to inform, enlighten, stimulate, guide, correct, inspire, entertain and encourage.” Similarly to the original that “The emphasis of the articles is on teaching and the classroom”, the key messages in the current Aims and Scope of Teaching Statistics are that this journal is “......intended for all those who teach statistics and data science ....... The emphasis is on good practice in teaching statistics, statistical thinking and data science in any context.......”. The initial support for the journal [1] was provided by four professional organisations, the International Statistical Institute (ISI), the Royal Statistical Society (RSS), the Institute of Statisticians (merged with the RSS in 1993), and the Applied Probability Trust (APT). The support and involvement of these professional organisations are indicative of the focus on education by the whole statistical community in the 1960's and 1970's. When the new United Nations took over many of the previous government-oriented responsibilities of the International Statistical Institute (ISI), the ISI took on more general professional roles, including setting up its Education Committee in 1948. Although the initial educational focus was on training in official statistics, particularly in developing countries, as described in [2,9], interest rapidly grew and broadened to university teaching, both for future statisticians and across disciplines, and then to schools. The chairpersons of the ISI Education Committee, and the topics of the Committee's Roundtable Meetings in the 1960's and 1970's (see [9]) reflect the intertwining work of leaders in the rapidly evolving and broadening discipline of the statistical sciences. Another indication was the establishment of the APT in 1964 at the University of Sheffield by Joseph Gani, Professor of Statistics at Sheffield 1965-1974, to publish the Journal of Applied Probability as an outlet for work on wide-ranging real problems, such as in genetics, epidemics, finance; applied probability is an integral and essential constituent of the broad tent of the statistical sciences. Statistics education and teaching must always evolve and broaden to reflect the growth and developments in our wideranging and increasingly vital discipline of the sciences of statistics, data and chance. The intertwining of statistical education developments can be seen in [1,2,9]. In the UK, the sett","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48396987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilities, odds, and vigorish","authors":"Joseph G. Eisenhauer","doi":"10.1111/test.12318","DOIUrl":"https://doi.org/10.1111/test.12318","url":null,"abstract":"This paper uses actual data on horse racing to illustrate probabilities, odds, and expected values, and offers cautionary remarks about applying textbook formulas to gambling on real‐world sporting events.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44249510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Writing goals in U.S. undergraduate data science course outlines: A textual analysis","authors":"Constance L. Gooding, Alex Lyford, G. Giaimo","doi":"10.1111/test.12314","DOIUrl":"https://doi.org/10.1111/test.12314","url":null,"abstract":"Instructors at postsecondary institutions have designed a myriad of data science classes to keep up with the rise of big data. Businesses and companies have become increasingly interested in hiring people with strong data acquisition, management, and communication skills. Since data science as a field of study is relatively new, though it has deep connections to statistical studies, there are few comprehensive analyses of data science classes, majors, programs, and curricular goals. Through this research, we analyze how writing and communication are taught in undergraduate data science classes in the United States. We analyze the presence of writing and communication learning goals from course descriptions and course syllabi. These results show that most data science courses emphasize technical, computing skills over writing, and communication skills. We conclude with a set of actionable heuristics that emphasize integrating writing and communication into data science courses so that students are prepared to use these skills as responsible citizens.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49271717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UEFA EURO 2020: An exciting match between football and probability","authors":"Giulia Fedrizzi, Luisa Canal, Rocco Micciolo","doi":"10.1111/test.12315","DOIUrl":"https://doi.org/10.1111/test.12315","url":null,"abstract":"Football, as one of the most popular sports, can provide exciting examples to motivate students learning statistics. In this paper, we analyzed the number of goals scored in the UEFA EURO 2020 final phase as well as the waiting times between goals, considering censored times. Such a dataset allows us to consider some aspects of count data taught at an introductory level (such as the Poisson distribution), as well as more advanced topics (such as survival analysis taking into account the presence of censored times). Employing data from the final phase of UEFA EURO 2020, depending on the course level, the student will acquire knowledge and understanding of a range of key topics and analytical techniques in statistics, develop knowledge of the theoretical assumption underlying them and learn the skills needed to model count data.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46264711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An applied statistics teaching lesson that uses NBA playoff data to illustrate uncertainty in sporting contests","authors":"Rotua Lumbantobing, Todd McFall","doi":"10.1111/test.12313","DOIUrl":"https://doi.org/10.1111/test.12313","url":null,"abstract":"In this article, we offer a teaching lesson on combinatorics and binary outcomes that utilizes real‐world data. The focus of the lesson is to teach students how to analyze the effects of the National Basketball Association's (NBA) 2003 decision to extend the first round of its postseason from a best‐of‐five series of games to a best‐of‐seven series using combinatorics and ideas about binary outcomes. Students conjecture how much longer series will make less certain the outcomes of these series and then use the 27 years of first‐round series results we provide to evaluate their conjectures on how series results have changed since 2003. After finishing this lesson, students will have a firmer grasp on applying combinatorics and binary outcomes to real‐world situations. This lesson is compatible with both traditional and remote classes and can be extended to other sports, making it a lesson for all academic seasons.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46737253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical edutainment: You're the subject for our next subject!","authors":"L. Lesser, Dennis K. Pearl","doi":"10.1111/test.12312","DOIUrl":"https://doi.org/10.1111/test.12312","url":null,"abstract":"Readers are invited to participate in a data collection exercise that will be used subsequently in this series.","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45623489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Comment","authors":"Kassandra E. Zaila Ardines, Petar Bajic","doi":"10.1111/test.12047","DOIUrl":"https://doi.org/10.1111/test.12047","url":null,"abstract":"© 2014 The Authors Teaching Statistics © 2014 Te Teaching Statistics hats off to the G Oswald George prize winners 2013 for focussing attention on problems with excessive use of the term ‘population’ in statistics education and discussion of possible alternative approaches. In their article, Lu and Henning (2013) quote an oftenused classical definition of a population as ‘the set of all entities of interest in a particular statistical study’, discuss what is wrong with the population concept, include suggestions of terms such as ‘data generating process’ and the ‘statistical population’ of Kass (2011), and emphasize the importance of understanding and questioning the assumptions of a statistical model, and how a statistical model is a ‘lens’ (Wild, 2006). There is much discussion that can be opened here. Is the prolonged use of the word ‘population’ just habit, or is it because of avoiding words such as ‘theoretical’, ‘model’, ‘distribution’ or ‘parameter’, and lack of energy to find alternative expressions suitable for various student levels? Lu and Henning point to the problem of understanding randomness in experiments; the challenges of observational studies require even more discussion with students. In coming to understand concepts of random representativeness of data with respect to the issues of interest, the words ‘general situation or population’ can be very useful. For example, ‘if the data can be considered randomly representative of a general situation or population with respect to the issues of interest, then we can use the data to comment on the general situation or population with respect to these issues’. Clearly the word ‘population’ is appropriate for some statistical situations, particularly those involving sampling from real populations of entities whether they be marbles or people. Such situations tend to be how young students are introduced to both chance and data. Then the term ‘population proportion’, which does make sense, tends to appear and the challenges of sampling to estimate a population proportion provide rich scenarios for student thinking and learning, with focus on ‘worry’ questions rather than delusions created by emphasis on sampling schemes available to organisations with vast resources. Categorical data do indeed provide valuable doors to statistical concepts and thinking at a","PeriodicalId":43739,"journal":{"name":"Teaching Statistics","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1111/test.12047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49207640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}